1. Field of the Invention
The present invention relates generally to systems and methods for flaw detection in physical structures, especially high value asset structures. The present invention relates more specifically to systems and methods for flaw detection and monitoring at elevated temperatures with wireless communication using surface embedded, monolithically integrated, thin-film, magnetically actuated sensors, and additionally to methods for fabricating sensors used in such systems.
2. Description of the Related Art
High quality, robust sensors capable of on-board detection and monitoring of damage would result in significant enhancements to the safety, reliability, and availability of high value assets, while minimizing their total life cycle costs. For highly stressed, fatigue-critical components, such as rotating components in turbines and rotorcraft, one would ideally like to obtain a direct measure of the state of damage in the material. However, obtaining such a measurement presents numerous technical challenges arising from the thermal and stress environments, combined with the high rotational speed and limited accessibility of such systems. Consequently, it is not surprising that there are presently no operational sensors for direct measurement of material damage (cracking) during component operation.
Attempts have also been made to use acoustic emission (AE) sensors to extract cracking signatures from the numerous sources of acoustic activity that accompany component operation. Although vibration and AE measurements are relatively easy to make, analyzing the results in order to extract the cracking signature from the overall rotor dynamics, or the acoustic background, continues to be a significant challenge.
A variety of nondestructive evaluation (NDE) techniques have been developed and refined for measurement of cracks to relatively small sizes (0.020 in.-0.030 in.) in depot based inspections. However, these techniques require component disassembly and a relatively well controlled environment; consequently they are not adaptable to on-line sensing in the operating environment. The development paradigm for such depot NDE techniques is very different from that needed to develop functional on-board sensors. Traditional depot-type inspections are driven by economics, which dictate that they be done relatively infrequently, and thus with high sensitivity, to ensure that damage (cracks) are small so that the component can survive until the next inspection, which is often ten years or more in the future.
The science and technology of prognosis and structural health management offer the potential for significant enhancements in the safety, reliability and readiness of high-value assets. For the case of turbine engines, this concept is based on a closed-loop process whose successful implementation depends on the integration of several multidisciplinary elements including: 1) onboard sensing of operational parameters and material damage states; 2) diagnosing trends, fault conditions, and underlying damage; 3) prognosing (predicting) remaining useful life in terms of probability of failure and limits on reliable performance, and 4) deciding upon appropriate courses of action. For example, whether or not the asset is capable of performing a given mission, or alternatively, is in need of inspection, maintenance, or replacement. As indicated, a wide variety of hardware and software tools are needed to facilitate these process steps. However, considerable uncertainty exists in the usage and sensor inputs, as well as the required modeling and associated materials property inputs. Consequently, there is an inherent need for the reasoning element of the prognosis system to be probabilistically-based.
Complementing the variety of onboard sensors are traditional health monitoring software tools for pattern recognition, neural networks, Bayesian updating, expert systems, and fuzzy logic. The advantage of these tools is that, when properly applied, they are highly efficient and thus amenable to onboard monitoring and real-time data interpretation. However, the disadvantage of these tools is that they rarely involve consideration of the underlying physical processes. Consequently, they require considerable empirical calibration or “training” for each specific application of interest. In contrast, probabilistic life prediction is typically based on materials property data, finite element thermal and stress analysis, pre-service inspection and in-service monitoring for defects, and damage accumulation algorithms. The advantage of this approach is that it is more amenable to linkage with the underlying physical mechanisms of damage (i.e., crack nucleation and growth). Thus, the process is inherently suitable for extension into materials prognosis, a concept that combines information on the material damage state with mechanistically-based predictive models.
The fundamental goal of all of these approaches is to facilitate better-informed decisions, whether for mission planning in the field (over the short term), or sustainment at the depot (over the longer term). In fact, the optimum prognosis system is likely to be some combination of traditional data-driven methods and probabilistic mechanics methods. Thus, in many respects the above tools can be viewed as being complementary.
With regard to on-board crack detection in fatigue-critical components, the important question becomes: What detection sensitivity is sufficient to provide the desired component reliability, provided essentially continuous inspections can be conducted, either during or after each operation cycle? Studies have been carried out involving, for example, probabilistic simulation of low-cycle fatigue crack initiation and growth at a bolt hole of a typical compressor disc in a military turbine engine. Predicted probabilities of failure over the life of the disc have been evaluated for various inspection scenarios ranging from no inspections to continual inspections with varying sensitivities. The probability of failure under such conditions begins to increase first for the case where no inspection is performed. In contrast, inspections performed continually (i.e. once every flight) result in markedly lower probability of failure even with relatively coarse inspection sensitivities of 200 to 300 mils (in size). For these cases, acceptable probabilities of failure are maintained by inspecting on each flight and removing defective discs from service. The results obtained under these studies show that sensitivities of 200 to 300 mils can be effective for on-board monitoring for cases where critical crack sizes exceed these values. Continual monitoring with sensitivities 10 times lower than those typically employed in depot inspections (20-30 mils) are effective because of the trade-off between inspection sensitivity and inspection frequency. In other words, on-board inspections do not require high sensitivity to be effective because they only need to find cracks that will not grow to failure in the next few flights. Similar benefits of continual on-board monitoring are anticipated for fatigue critical components, although specific results will obviously depend on the critical crack size in the component, and thus will be component dependent.
It would therefore be desirable to have a system (and a method of operating the system) that is capable of on-board detection and monitoring of cracks in critical structures with a sensitivity that is commensurate with the frequency of interrogation made possible by the system. It would be desirable for such a system to utilize a sensor structure that is robust enough to withstand the vibrational and thermal extremes typically experienced within such high-value asset systems (such as turbines and rotors). It would therefore be desirable to include wireless connectivity to and from the sensor structure(s) that could operate within the high level EM noise environment of rotating metal components. It would further be desirable to provide a versatile sensor manufacturing process that could create customized sensors suitable for specific structural systems and specific operating environments.